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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林菀俞(Wan-Yu Lin) | |
| dc.contributor.author | Chi Liu | en |
| dc.contributor.author | 劉錡 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:34:31Z | - |
| dc.date.copyright | 2021-10-23 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-20 | |
| dc.identifier.citation | An, J., Gharahkhani, P., Law, M. H., Ong, J.-S., Han, X., Olsen, C. M., Neale, R. E., Lai, J., Vaughan, T. L., Gockel, I., Thieme, R., Böhmer, A. C., Jankowski, J., Fitzgerald, R. C., Schumacher, J., Palles, C., Gammon, M. D., Corley, D. A., Shaheen, N. J., Bird, N. C., Hardie, L. J., Murray, L. J., Reid, B. J., Chow, W.-H., Risch, H. A., Ye, W., Liu, G., Romero, Y., Bernstein, L., Wu, A. H., Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McIntyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A. M., Pitts, S. J., Sathirapongsasuti, J. F., Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Whiteman, D. C., MacGregor, S., Beacon, andMe Research, T. (2019). Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases. Nature Communications, 10(1), 4219. doi:10.1038/s41467-019-11968-2 Aryee, M. J., Jaffe, A. E., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A. P., Hansen, K. D., Irizarry, R. A. (2014). Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics (Oxford, England), 30(10), 1363-1369. doi:10.1093/bioinformatics/btu049 Bell, J. T., Pai, A. A., Pickrell, J. K., Gaffney, D. J., Pique-Regi, R., Degner, J. F., Gilad, Y., Pritchard, J. K. (2011). DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biology, 12(1), R10. doi:10.1186/gb-2011-12-1-r10 Bourc'his, D., Xu, G.-L., Lin, C.-S., Bollman, B., Bestor, T. H. (2001). Dnmt3L and the establishment of maternal genomic imprints. Science, 294(5551), 2536. doi:10.1126/science.1065848 Boyce, W. T., Sokolowski, M. B., Robinson, G. E. (2020). Genes and environments, development and time. Proceedings of the National Academy of Sciences, 117(38), 23235. doi:10.1073/pnas.2016710117 Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., Ouwehand, W. H., Samani, N. J., Todd, J. A., Donnelly, P., Barrett, J. C., Burton, P. R., Davison, D., Donnelly, P., Easton, D., Evans, D., Leung, H.-T., Marchini, J. L., Morris, A. P., Spencer, C. C. A., Tobin, M. D., Cardon, L. R., Clayton, D. G., Attwood, A. P., Boorman, J. P., Cant, B., Everson, U., Hussey, J. M., Jolley, J. D., Knight, A. S., Koch, K., Meech, E., Nutland, S., Prowse, C. V., Stevens, H. E., Taylor, N. C., Walters, G. R., Walker, N. M., Watkins, N. A., Winzer, T., Todd, J. A., Ouwehand, W. H., Jones, R. W., McArdle, W. L., Ring, S. M., Strachan, D. P., Pembrey, M., Breen, G., St Clair, D., Caesar, S., Gordon-Smith, K., Jones, L., Fraser, C., Green, E. K., Grozeva, D., Hamshere, M. L., Holmans, P. A., Jones, I. R., Kirov, G., Moskvina, V., Nikolov, I., O'Donovan, M. C., Owen, M. J., Craddock, N., Collier, D. A., Elkin, A., Farmer, A., Williamson, R., McGuffin, P., Young, A. H., Ferrier, I. N., Ball, S. G., Balmforth, A. J., Barrett, J. H., Bishop, D. T., Iles, M. M., Maqbool, A., Yuldasheva, N., Hall, A. S., Braund, P. S., Burton, P. R., Dixon, R. J., Mangino, M., Stevens, S., Tobin, M. D., Thompson, J. R., Samani, N. J., Bredin, F., Tremelling, M., Parkes, M., Drummond, H., Lees, C. W., Nimmo, E. R., Satsangi, J., Fisher, S. A., Forbes, A., Lewis, C. M., Onnie, C. M., Prescott, N. J., Sanderson, J., Mathew, C. G., Barbour, J., Mohiuddin, M. K., Todhunter, C. E., Mansfield, J. C., Ahmad, T., Cummings, F. R., Jewell, D. P., Webster, J., Brown, M. J., Clayton, D. G., Lathrop, G. M., Connell, J., Dominiczak, A., Samani, N. J., Marcano, C. A. B., Burke, B., Dobson, R., Gungadoo, J., Lee, K. L., Munroe, P. B., Newhouse, S. J., Onipinla, A., Wallace, C., Xue, M., Caulfield, M., Farrall, M., Barton, A., and Genomics, T. B. i. R. A. G., Bruce, I. N., Donovan, H., Eyre, S., Gilbert, P. D., Hider, S. L., Hinks, A. M., John, S. L., Potter, C., Silman, A. J., Symmons, D. P. M., Thomson, W., Worthington, J., Clayton, D. G., Dunger, D. B., Nutland, S., Stevens, H. E., Walker, N. M., Widmer, B., Todd, J. A., Frayling, T. M., Freathy, R. M., Lango, H., Perry, J. R. B., Shields, B. M., Weedon, M. N., Hattersley, A. T., Hitman, G. A., Walker, M., Elliott, K. S., Groves, C. J., Lindgren, C. M., Rayner, N. W., Timpson, N. J., Zeggini, E., McCarthy, M. I., Newport, M., Sirugo, G., Lyons, E., Vannberg, F., Hill, A. V. S., Bradbury, L. A., Farrar, C., Pointon, J. J., Wordsworth, P., Brown, M. A., Franklyn, J. A., Heward, J. M., Simmonds, M. J., Gough, S. C. L., Seal, S., Susceptibility Collaboration, B. C., Stratton, M. R., Rahman, N., Ban, M., Goris, A., Sawcer, S. J., Compston, A., Conway, D., Jallow, M., Newport, M., Sirugo, G., Rockett, K. A., Kwiatkowski, D. P., Bumpstead, S. J., Chaney, A., Downes, K., Ghori, M. J. R., Gwilliam, R., Hunt, S. E., Inouye, M., Keniry, A., King, E., McGinnis, R., Potter, S., Ravindrarajah, R., Whittaker, P., Widden, C., Withers, D., Deloukas, P., Leung, H.-T., Nutland, S., Stevens, H. E., Walker, N. M., Todd, J. A., Easton, D., Clayton, D. G., Burton, P. R., Tobin, M. D., Barrett, J. C., Evans, D., Morris, A. P., Cardon, L. R., Cardin, N. J., Davison, D., Ferreira, T., Pereira-Gale, J., Hallgrimsdóttir, I. B., Howie, B. N., Marchini, J. L., Spencer, C. C. A., Su, Z., Teo, Y. Y., Vukcevic, D., Donnelly, P., Bentley, D., Brown, M. A., Cardon, L. R., Caulfield, M., Clayton, D. G., Compston, A., Craddock, N., Deloukas, P., Donnelly, P., Farrall, M., Gough, S. C. L., Hall, A. S., Hattersley, A. T., Hill, A. V. S., Kwiatkowski, D. P., Mathew, C. G., McCarthy, M. I., Ouwehand, W. H., Parkes, M., Pembrey, M., Rahman, N., Samani, N. J., Stratton, M. R., Todd, J. A., Worthington, J., The Wellcome Trust Case Control, C., Management, C., Data, Analysis, C., Services, U. K. B., University of Cambridge, C., Birth Cohort, C., Bipolar, D., Coronary Artery, D., Crohn’s, D., Hypertension, Rheumatoid, A., Type, D., Type, D., Tuberculosis, Ankylosing, S., Autoimmune Thyroid, D., Breast, C., Multiple, S., Gambian, C., Dna, G. D. Q. C., Informatics, Statistics, Primary, I. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661-678. doi:10.1038/nature05911 Carlsson, S., Hammar, N., Grill, V., Kaprio, J. (2003). Alcohol consumption and the incidence of type 2 diabetes. Diabetes Care, 26(10), 2785. doi:10.2337/diacare.26.10.2785 Chen, C.-H., Yang, J.-H., Chiang, C. W. K., Hsiung, C.-N., Wu, P.-E., Chang, L.-C., Chu, H.-W., Chang, J., Song, I. W., Yang, S.-L., Chen, Y.-T., Liu, F.-T., Shen, C.-Y. (2016). Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Human Molecular Genetics, 25(24), 5321-5331. doi:10.1093/hmg/ddw346 Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H., Nadeau, J. H. (2010). Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics, 11(6), 446-450. doi:10.1038/nrg2809 Fortin, J.-P., Triche, T. J., Jr., Hansen, K. D. (2017). Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics, 33(4), 558-560. doi:10.1093/bioinformatics/btw691 Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., Im, H. K., Consortium, G. T. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098. doi:10.1038/ng.3367 Gaunt, T. R., Shihab, H. A., Hemani, G., Min, J. L., Woodward, G., Lyttleton, O., Zheng, J., Duggirala, A., McArdle, W. L., Ho, K., Ring, S. M., Evans, D. M., Davey Smith, G., Relton, C. L. (2016). Systematic identification of genetic influences on methylation across the human life course. Genome Biology, 17(1), 61. doi:10.1186/s13059-016-0926-z GTEx Consortium. (2015). The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science, 348(6235), 648. doi:10.1126/science.1262110 Hamilton, J. P. (2011). Epigenetics: principles and practice. Digestive diseases, 29(2), 130-135. Hannon, E., Spiers, H., Viana, J., Pidsley, R., Burrage, J., Murphy, T. M., Troakes, C., Turecki, G., O'Donovan, M. C., Schalkwyk, L. C., Bray, N. J., Mill, J. (2016). Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nature Neuroscience, 19(1), 48-54. doi:10.1038/nn.4182 Herman, J. G., Baylin, S. B. (2003). Gene silencing in cancer in association with promoter hypermethylation. New England Journal of Medicine, 349(21), 2042-2054. doi:10.1056/NEJMra023075 Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), 3156. doi:10.1186/gb-2013-14-10-r115 Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., Kelsey, K. T. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13(1), 86. doi:10.1186/1471-2105-13-86 Jaffe, A. E., Irizarry, R. A. (2014). Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology, 15(2), R31. doi:10.1186/gb-2014-15-2-r31 Khuri, A. I. (2010). Linear Model Methodology. Boca Raton: CRC Press. Klengel, T., Mehta, D., Anacker, C., Rex-Haffner, M., Pruessner, J. C., Pariante, C. M., Pace, T. W. W., Mercer, K. B., Mayberg, H. S., Bradley, B., Nemeroff, C. B., Holsboer, F., Heim, C. M., Ressler, K. J., Rein, T., Binder, E. B. (2013). Allele-specific FKBP5 DNA demethylation mediates gene–childhood trauma interactions. Nature Neuroscience, 16(1), 33-41. doi:10.1038/nn.3275 Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., Whitsel, E. A., Wilson, J. G., Reiner, A. P., Aviv, A., Lohman, K., Liu, Y., Ferrucci, L., Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573-591. doi:10.18632/aging.101414 Ligthart, S., Steenaard, R. V., Peters, M. J., van Meurs, J. B. J., Sijbrands, E. J. G., Uitterlinden, A. G., Bonder, M. J., Hofman, A., Franco, O. H., Dehghan, A., consortium, B. (2016). Tobacco smoking is associated with DNA methylation of diabetes susceptibility genes. Diabetologia, 59(5), 998-1006. doi:10.1007/s00125-016-3872-0 Lin, W.-Y., Liu, Y.-L., Yang, A. C., Tsai, S.-J., Kuo, P.-H. (2020). Active cigarette smoking is associated with an exacerbation of genetic susceptibility to diabetes. Diabetes, 69(12), 2819-2829. doi:10.2337/db20-0156 Liu, J. Z., McRae, A. F., Nyholt, D. R., Medland, S. E., Wray, N. R., Brown, K. M., Hayward, N. K., Montgomery, G. W., Visscher, P. M., Martin, N. G., Macgregor, S. (2010). A versatile gene-based test for genome-wide association studies. The American Journal of Human Genetics, 87(1), 139-145. doi:https://doi.org/10.1016/j.ajhg.2010.06.009 Maas, S. C. E., Mens, M. M. J., Kühnel, B., van Meurs, J. B. J., Uitterlinden, A. G., Peters, A., Prokisch, H., Herder, C., Grallert, H., Kunze, S., Waldenberger, M., Kavousi, M., Kayser, M., Ghanbari, M. (2020). Smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic traits. Clinical Epigenetics, 12(1), 157. doi:10.1186/s13148-020-00951-0 Maddatu, J., Anderson-Baucum, E., Evans-Molina, C. (2017). Smoking and the risk of type 2 diabetes. Translational Research, 184, 101-107. doi:https://doi.org/10.1016/j.trsl.2017.02.004 Marabita, F., Almgren, M., Sjöholm, L. K., Kular, L., Liu, Y., James, T., Kiss, N. B., Feinberg, A. P., Olsson, T., Kockum, I., Alfredsson, L., Ekström, T. J., Jagodic, M. (2017). Smoking induces DNA methylation changes in Multiple Sclerosis patients with exposure-response relationship. Scientific Reports, 7(1), 14589. doi:10.1038/s41598-017-14788-w Marco, C., Antonio, D., Antonina, S., Alessandro, S., Concetta, C. (2015). Genes involved in pruning and inflammation are enriched in a large mega-sample of patients affected by Schizophrenia and Bipolar Disorder and controls. Psychiatry Research, 228(3), 945-949. doi:https://doi.org/10.1016/j.psychres.2015.06.013 McClay, J. L., Shabalin, A. A., Dozmorov, M. G., Adkins, D. E., Kumar, G., Nerella, S., Clark, S. L., Bergen, S. E., Hultman, C. M., Magnusson, P. K. E., Sullivan, P. F., Aberg, K. A., van den Oord, E. J. C. G., Swedish Schizophrenia, C. (2015). High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction. Genome Biology, 16(1), 291. doi:10.1186/s13059-015-0842-7 Palmer, C. J., Bruckner, R. J., Paulo, J. A., Kazak, L., Long, J. Z., Mina, A. I., Deng, Z., LeClair, K. B., Hall, J. A., Hong, S., Zushin, P. H., Smith, K. L., Gygi, S. P., Hagen, S., Cohen, D. E., Banks, A. S. (2017). Cdkal1, a type 2 diabetes susceptibility gene, regulates mitochondrial function in adipose tissue. Mol Metab, 6(10), 1212-1225. doi:10.1016/j.molmet.2017.07.013 Pascoe, L., Tura, A., Patel, S. K., Ibrahim, I. M., Ferrannini, E., Zeggini, E., Weedon, M. N., Mari, A., Hattersley, A. T., McCarthy, M. I., Frayling, T. M., Walker, M. (2007). Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are associated with decreased pancreatic β-cell function. Diabetes, 56(12), 3101. doi:10.2337/db07-0634 Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208. doi:10.1186/s13059-016-1066-1 Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559-575. doi:https://doi.org/10.1086/519795 Russo, V. E., Martienssen, R. A., Riggs, A. D. (1996). Epigenetic mechanisms of gene regulation: Cold Spring Harbor Laboratory Press. Sheen, Y.-J., Hsu, C.-C., Jiang, Y.-D., Huang, C.-N., Liu, J.-S., Sheu, W. H.-H. (2019). Trends in prevalence and incidence of diabetes mellitus from 2005 to 2014 in Taiwan. Journal of the Formosan Medical Association, 118, S66-S73. doi:https://doi.org/10.1016/j.jfma.2019.06.016 Steinthorsdottir, V., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Jonsdottir, T., Walters, G. B., Styrkarsdottir, U., Gretarsdottir, S., Emilsson, V., Ghosh, S., Baker, A., Snorradottir, S., Bjarnason, H., Ng, M. C. Y., Hansen, T., Bagger, Y., Wilensky, R. L., Reilly, M. P., Adeyemo, A., Chen, Y., Zhou, J., Gudnason, V., Chen, G., Huang, H., Lashley, K., Doumatey, A., So, W.-Y., Ma, R. C. Y., Andersen, G., Borch-Johnsen, K., Jorgensen, T., van Vliet-Ostaptchouk, J. V., Hofker, M. H., Wijmenga, C., Christiansen, C., Rader, D. J., Rotimi, C., Gurney, M., Chan, J. C. N., Pedersen, O., Sigurdsson, G., Gulcher, J. R., Thorsteinsdottir, U., Kong, A., Stefansson, K. (2007). A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nature Genetics, 39(6), 770-775. doi:10.1038/ng2043 Teh, A. L., Pan, H., Chen, L., Ong, M.-L., Dogra, S., Wong, J., MacIsaac, J. L., Mah, S. M., McEwen, L. M., Saw, S.-M., Godfrey, K. M., Chong, Y.-S., Kwek, K., Kwoh, C.-K., Soh, S.-E., Chong, M. F. F., Barton, S., Karnani, N., Cheong, C. Y., Buschdorf, J. P., Stünkel, W., Kobor, M. S., Meaney, M. J., Gluckman, P. D., Holbrook, J. D. (2014). The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Research, 24(7), 1064-1074. doi:10.1101/gr.171439.113 Triche, T. J., Jr, Weisenberger, D. J., Van Den Berg, D., Laird, P. W., Siegmund, K. D. (2013). Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Research, 41(7), e90-e90. doi:10.1093/nar/gkt090 Unoki, H., Takahashi, A., Kawaguchi, T., Hara, K., Horikoshi, M., Andersen, G., Ng, D. P. K., Holmkvist, J., Borch-Johnsen, K., Jørgensen, T., Sandbæk, A., Lauritzen, T., Hansen, T., Nurbaya, S., Tsunoda, T., Kubo, M., Babazono, T., Hirose, H., Hayashi, M., Iwamoto, Y., Kashiwagi, A., Kaku, K., Kawamori, R., Tai, E. S., Pedersen, O., Kamatani, N., Kadowaki, T., Kikkawa, R., Nakamura, Y., Maeda, S. (2008). SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nature Genetics, 40(9), 1098-1102. doi:10.1038/ng.208 Willi, C., Bodenmann, P., Ghali, W. A., Faris, P. D., Cornuz, J. (2007). Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA, 298(22), 2654-2664. doi:10.1001/jama.298.22.2654 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82258 | - |
| dc.description.abstract | " 抽菸已被證實為與肺癌、代謝症候群等疾病相關之環境因子。然而，其影響基因表現以及病因之相關性尚待研究。而 DNA 甲基化 （DNA methylation）作為一廣泛存在於各物種間的生化機制，已有研究證實其與發育、老化和多種疾病具有相關性。其具可遺傳且可變的特性亦使之成為研究抽菸與生化機制間交互作用的重要因子之一。藉由當今所開發的 EPIC 生物晶片，我們得以族群的規模針對 DNA 甲基化進行全表觀基因體關聯分析，然而目前對於台灣族群的基因變異於 DNA 甲基化之影響並未有透徹的研究。吾人可偕同由單核苷酸多型性（SNPs）所做的全基因體關聯分析，研究其在全表觀基因體關聯分析中基因變異、環境以及其交互作用對於 DNA 甲基化的影響程度。 本研究針對台灣人體生物資料庫所提供之近 2,000 個個體中糖尿病易感之基因以及其鄰近區域進行研究。穩健的全表觀基因體關聯分析結果顯示有 6 個位點與抽菸有關，然而僅有分散於 3 個基因上的 5 個位點具有甲基化數量性狀基因座（mQTL）。而 mQTL 為基因序列中固有之變異，因此其對於甲基化程度的影響相對於環境所造成的影響較為穩定。從而在進行全表觀基因體關聯分析時也應納入此變項。綜合上述於台灣人體資料庫中所發現 5 個具有甲基化數量基因座的位點以及 6 種不同的吸菸變項如：是否抽菸、抽菸持續時間、吸菸數量、每年吸菸包數、曾經每年吸菸包數以及二手菸。本研究藉由型一迴歸平方和（type I sum of squares）分解，定義被解釋的變異百分比。在此前提下，吾人發現不論何種抽菸行為，cg01744331、cg16556677、cg26963277、cg23161492 、cg03450842 的甲基化數量基因座皆解釋了多數的變異量。而其中與正在抽菸相關的環境變項具有較高的變異解釋程度，曾經每年吸菸包數與二手菸的變異解釋程度則較低。另外，本研究發現不論是否為具有較高解釋程度的環境變項，其與甲基化數量基因座的交互作用皆接近於零。而吸菸相關環境變項皆在基因 KCNQ1 上的 cg26963277 則具有高於甲基化數量基因座的傾向，也因此其本身可能為在進行環境與糖尿病發展交互作用推論中具有決定性的位點。此外，本研究所提出的平方和分解百分比亦可作為日後研究不同效應作用於生物學變異上之推論指標。 " | zh_TW |
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| dc.description.tableofcontents | 口試委員會審定書 i 致謝 ii 摘要 iii Abstract v Content vii List of Figures ix List of Tables x Chapter 1 Introduction 1 Chapter 2 Materials and Methods 4 2.1 Research Data 4 2.1.1 Ethics Declaration 4 2.1.2 Taiwan Biobank 4 2.1.3 Quality Control of Genotypic Data 5 2.1.4 Imputation of Genotypic Data 6 2.1.5 Quality Control of Methylation Data 6 2.1.6 Selection of Methylation Sites 7 2.2 Statistical Analysis 8 2.2.1 Study Panel 8 2.2.2 Active Smoking Main Effect Epigenome-wide Association Study 8 2.2.3 Epignome-wide Association Study on Serum Fasting Glucose 9 2.2.4 Genome-wide Association Study 9 2.2.5 Explained Proportion by Sum of Squares 10 Chapter 3 Results 14 3.1 Basic Characteristic of TWB 14 3.2 Association Studies 14 3.2.1 Epigenome-wide Association Study 14 3.2.2 Robust Epigenome-wide Association Study 15 3.2.3 Identified Epigenetic Sites Associated with Serum Fasting Glucose 15 3.2.4 Genome-wide Association Study 15 3.2.5 Effect of Active Smoking on Differentially Methylated Sites 16 3.2.6 Explained Percentage of mQTL and Environment on β-value 17 Chapter 4 Discussion 18 Figures 22 Tables 25 References 30 | |
| dc.language.iso | en | |
| dc.subject | mQTL | zh_TW |
| dc.subject | 糖尿病 | zh_TW |
| dc.subject | 型一平方和 | zh_TW |
| dc.subject | 混合效應模型 | zh_TW |
| dc.subject | DNA 甲基化 | zh_TW |
| dc.subject | type I sum of squares | en |
| dc.subject | Diabetes | en |
| dc.subject | DNA methylation | en |
| dc.subject | mQTL | en |
| dc.subject | Mixed effects model | en |
| dc.title | 抽菸以及遺傳變異效應於糖尿病易感受基因 DNA 甲基化程度之衡估 | zh_TW |
| dc.title | An evaluation of active cigarette smoking and genetic variants on DNA methylation of diabetes susceptibility genes | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭柏秀(Hsin-Tsai Liu),盧子彬(Chih-Yang Tseng) | |
| dc.subject.keyword | 糖尿病,DNA 甲基化,mQTL,混合效應模型,型一平方和, | zh_TW |
| dc.subject.keyword | Diabetes,DNA methylation,mQTL,Mixed effects model,type I sum of squares, | en |
| dc.relation.page | 34 | |
| dc.identifier.doi | 10.6342/NTU202103868 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-10-20 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| dc.date.embargo-lift | 2024-09-01 | - |
| 顯示於系所單位： | 統計碩士學位學程 | |
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